Multi-Dimensional Feature Adaptive Gating Network for Multi-View and Whole-Brain Magnetic Resonance Imaging-Based Alzheimer’s Disease Classification

Summary

This protocol presents a reproducible, explainable deep learning pipeline for Alzheimer’s disease classification using multidimensional structural MRI representations. The framework integrates multiview slice-level features and whole-brain three-dimensional features through adaptive sparse aggregation, dynamic feature fusion, and anatomical interpretability analysis.

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder associated with structural brain alterations detectable by structural magnetic resonance imaging (sMRI). Accurate classification of normal control (NC), mild cognitive impairment (MCI), and AD remains challenging because disease-related changes are subtle, spatially distributed across multiple brain regions, and difficult to interpret in a biologically meaningful manner. Existing deep learning methods often rely on either whole-brain volumetric features or two-dimensional slice-level representations, which may limit the integration of complementary structural information and reduce model interpretability. This study presents a reproducible and explainable deep learning framework, termed the multidimensional feature adaptive gating network (MDFAG-Net), for sMRI-based AD classification and neuroimaging analysis. The framework integrates multidimensional structural MRI representations by combining 2D slice-level features extracted from sagittal, coronal, and axial views using a Vision Transformer backbone with global whole-brain features extracted using a three-dimensional convolutional neural network. An Enhanced Sparse Multiple Instance Learning module is employed to identify informative slices and suppress redundant information in long slice sequences, while an adaptive gating module dynamically fuses multiview and whole-brain features according to sample-specific feature contributions. The framework was evaluated on 842 subjects from the Alzheimer’s Disease Neuroimaging Initiative dataset for NC/MCI/AD classification. MDFAG-Net demonstrated competitive classification performance and outperformed representative two-dimensional, three-dimensional, and multibranch comparison models under the same experimental setting. The model achieved an area under the receiver operating characteristic curve of 0.798. Furthermore, Grad-CAM and DeepSHAP analyses revealed anatomically relevant regions associated with AD progression, including the hippocampus, amygdala, posterior cingulate gyrus, parahippocampal gyrus, and ventricular system.

Introduction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in older adults. The disease is characterized by progressive cognitive decline associated with pathological alterations, including β-amyloid deposition and tau-related neurofibrillary degeneration¹. As AD progresses, impairments in memory, language, executive function, judgment, and daily living ability become increasingly severe². Mild cognitive impairment (MCI) is widely regarded as a transitional stage between normal aging and AD, and effective early intervention during this stage may delay disease progression³. Therefore, accurate identification of AD-related structural abnormalities is important for early diagnosis and disease management. Structural magnetic resonance imaging (sMRI) provides non-invasive visualization of brain morphology and has become an important imaging modality for investigating AD-related structural changes⁴.

Early sMRI-based studies commonly used voxel-based morphometry and handcrafted imaging biomarkers to measure disease-related brain morphological differences⁵. These approaches can identify structural alterations in the hippocampus, subcortical structures, and cortical regions, but they often depend on complex preprocessing procedures and expert-designed feature extraction. Feature selection and computational intelligence methods have also been used to reduce redundancy and improve classification performance in high-dimensional medical data^6,7. These studies provide useful methodological insights into medical feature screening, although task-specific adaptation and validation are required for sMRI-based neuroimaging applications.

With the development of deep learning, 3D-CNNs have been widely used to learn volumetric representations directly from whole-brain sMRI, including 3D-ResNet, 3D-DenseNet, and networks for brain-region segmentation^8,9,10,11. Subsequent studies extended whole-brain modeling through spatial-context learning, CNN–Transformer architectures, and transfer-learning strategies^12,13,14, while 3D-Swin-Transformer and BrainMAE further advanced volumetric representation learning through hierarchical attention and self-supervised pretraining^15,16. However, whole-brain representations may not sufficiently emphasize fine-grained pathological information. Patch- and ROI-based approaches have therefore been explored to capture localized abnormalities^17,18,19. For example, PDMML uses local patch representations to complement global features³, while ROI-based methods focus on AD-related anatomical structures such as the hippocampus and cortical regions. Although these approaches enhance local structural characterization, they often rely on additional segmentation, predefined regions, or domain-specific prior knowledge.

Two-dimensional slice-based methods have been widely used to capture local structural information from sMRI. Multiview frameworks aggregate axial, coronal, and sagittal slice features for subject-level classification²⁰, while Vision Transformer-based methods enhance slice representation through long-range dependency modeling²¹. PMF-Net further introduces multislice attention and personalized fusion across three anatomical views²². However, slice-based representations weaken 3D spatial context and may contain redundant or less informative slices^23,24. Therefore, adaptive selection of informative slices while preserving whole-brain structural information remains an important challenge in multiview sMRI analysis.

Several studies have combined multiview 2D slice information with whole-brain 3D features. For example, MSA3D jointly learns multiview 2D and 3D representations to complement spatial information across dimensions⁵. However, existing fusion strategies often rely on concatenation, static weighting, or predefined rules^5,25, limiting their ability to adapt feature contributions across subjects. Complementary representation learning has also been explored in incomplete multimodal AD diagnosis through missing-modality imputation and disease-oriented neuroimage synthesis^26,27. Unlike these multimodal approaches, the present study focuses on T1-weighted sMRI and addresses adaptive slice selection and dynamic fusion of multiview 2D and whole-brain 3D representations within a single imaging modality.

Attention-based multiple instance learning (MIL) provides a useful strategy for weakly supervised instance selection by assigning adaptive importance weights to individual instances²⁸. In medical image analysis, MIL-based methods have been used to identify diagnostically relevant regions while reducing the influence of irrelevant information^29,30. Sparse attention mechanisms further enhance discriminative instance selection by promoting sparse weighting and suppressing redundant observations³¹. These studies suggest that attention-based MIL with sparse selection may be suitable for aggregating long multiview sMRI slice sequences.

At the same time, dynamic gating mechanisms have been introduced to improve adaptive information flow and feature integration in neural networks. Early dynamic inference methods used gating or early-exit strategies to adjust computation according to input difficulty^32,33. More recently, dynamic multimodal fusion methods have used gating mechanisms to adaptively select modality-level and fusion-level representations³⁴. In AD-related multiview analysis, adaptive sparse multiview learning has also been explored to identify informative biomarkers under uncertainty³⁵. These studies indicate that adaptive gating may provide a flexible strategy for integrating heterogeneous structural features from different MRI dimensions.

To address these limitations, MDFAG-Net for sMRI-based AD classification was developed. MDFAG-Net integrates multiview 2D slice representations extracted from sagittal, coronal, and axial planes with whole-brain 3D structural representations. An Enhanced Sparse Multiple Instance Learning module is introduced to identify diagnostically informative slices and suppress redundant information through multiscale scoring and adaptive sparse selection. In addition, an adaptive gating module dynamically evaluates and fuses multiview 2D features and whole-brain 3D features to improve the utilization of complementary structural information. Using data from the Alzheimer’s Disease Neuroimaging Initiative, this study evaluates the effectiveness of adaptive slice selection and dynamic multidimensional feature fusion for NC/MCI/AD classification.

MDFAG-Net is designed for T1-weighted sMRI classification when multiview slice information and whole-brain structural features need to be jointly considered. Its adaptive slice aggregation and feature fusion are particularly suitable for reducing redundant slice information while preserving complementary representations across axial, coronal, sagittal, and whole-brain branches. For small datasets, scans with substantial artifacts, or studies without reliable spatial normalization, simpler single-view or region-based approaches may be more appropriate.

Protocol

All research procedures with human‑derived data followed the Declaration of Helsinki. MRI and clinical data were obtained from ADNI. Each ADNI site received IRB approval and written informed consent prior to participant enrollment. This study performs secondary computational analysis on existing fully de‑identified ADNI data accessed through official procedures. No subject recruitment, scanning, intervention, or access to personally identifiable information occurred. Therefore, separate institutional ethical review and additional approval numbers are not applicable. All data comply with the ADNI Data Use Agreement. Original ADNI ethical documents are cited in the Data Availability Statement.

1.Acquire MRI data and select subjects

1. Obtain MRI and clinical data
   Obtain T1-weighted structural MRI scans and corresponding clinical diagnoses from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database36. Download the imaging and clinical files through the official ADNI data-access process. Record the participant identifier, MRI acquisition date, diagnostic assessment date, diagnostic group, and scan file format in the data-processing log.
2. Apply the inclusion and exclusion criteria
   1. Include participants who underwent at least one T1-weighted volumetric MRI examination within 6 months of a clinically documented diagnosis37.
   2. Exclude MRI scans containing fewer than 60 slices.
   3. Exclude scans with incomplete brain coverage, corrupted files, or severe image artifacts that prevent reliable preprocessing.
   4. For participants with multiple eligible examinations, retain the MRI scan acquired closest to the corresponding diagnostic assessment.
   5. Retain only one MRI examination per participant to prevent participant-level data leakage between analysis subsets.
3. Assign diagnostic groups
   Retain one MRI examination per participant. Assign participants to the normal control (NC), mild cognitive impairment (MCI), or Alzheimer's disease (AD) group. 
4. Define the final cohort
   Construct the final cohort of 842 participants aged 60–90 years. Summarize the demographic characteristics in Table 1.

2.Preprocess MRI images

Perform all MRI preprocessing using the FMRIB Software Library (FSL, v6.0; University of Oxford, UK) and follow the imaging pipeline adapted from the UK Biobank study38. Use the T1-weighted NIfTI images as input and sequentially perform image reorientation, brain-tissue extraction, linear registration, nonlinear registration, bias-field correction, and atlas-based brain parcellation.

1. Preliminary reorientation, field-of-view cropping, and brain-tissue extraction
   1. Reorient each original T1-weighted MRI image to the standard anatomical orientation using the FSL reorientation tool.
   2. Estimate the robust field of view of the reoriented image using the FSL robust field-of-view tool. Crop the image according to the automatically determined three-dimensional boundaries.
   3. Extract the brain from the cropped image using the FSL Brain Extraction Tool (BET) with robust brain-center estimation. Use the default fractional intensity threshold of 0.5 and vertical gradient value of 0.
2. Registration, bias-field correction, and refined brain-tissue extraction
   1. Linearly register the cropped, brain-extracted image to the FSL MNI152 T1 1 mm brain template using the FSL Linear Image Registration Tool (FLIRT). Calculate and save the transformation matrix between the cropped native image space and the MNI152 standard space using the default FLIRT registration settings.
   2. Apply the linear transformation matrix obtained in Step 2.2.1 to the reoriented T1-weighted MRI image to generate a preliminarily registered image in MNI152 standard space.
   3. Perform a second brain-tissue extraction on the preliminarily registered image using BET with robust brain-center estimation. Set the fractional intensity threshold to 0.45 and the vertical-gradient value to 0.0.
   4. Linearly register the refined brain image to the FSL MNI152 T1 1 mm brain template using FLIRT with the default registration settings. Save the linearly registered brain image in uncompressed NIfTI format for subsequent processing.
   5. Perform nonlinear registration of the linearly registered T1-weighted MRI image using FSL FNIRT to estimate the nonlinear deformation field. Generate the corresponding inverse deformation field using FSL invwarp.
   6. Perform bias-field correction on the registered T1-weighted MRI image using FSL FAST with the -B option. Retain the restored image generated by FAST as the bias-field-corrected image for subsequent processing.
   7. Apply the MNI-space Hammersmith adult brain atlas to each final preprocessed MRI image. Use the atlas label image and the corresponding label-index file to parcellate the whole brain into 95 anatomically defined regions of interest in MNI152 standard space.
   8. Inspect representative MRI slices before and after preprocessing in the axial, coronal, and sagittal planes. Confirm consistent anatomical alignment, adequate removal of non-brain tissue, and consistent spatial standardization before subsequent feature extraction and classification. Present representative preprocessing results in Figure 1.

3.Build the MDFAG-Net architecture for Alzheimer's disease classification

The overall architecture of MDFAG-Net is shown in Figure 2. The framework integrates multiview MRI slice representations and whole-brain structural features for NC/MCI/AD classification.

1. Prepare multiview MRI slice sequences
   1. Load each preprocessed three-dimensional T1-weighted MRI volume and extract two-dimensional slices along the sagittal (x), coronal (y), and axial (z) planes. Denote the resulting slice sequences as S_x ,S_y , and S_z , respectively.
   2. Remove the slices beyond mid index ± N = 24 per orientation to build view specific candidate pools³⁹. Preserve the valid slices in native anatomical order, with pool sizes for sagittal, coronal, and axial views denoted as M_x, M_y , and M_z . Use N = 24 as a pre processing constraint to fully cover hippocampal anatomy in all three views.
   3. Convert every valid grayscale MRI slice to the three-channel input format required by the pretrained Vision Transformer. Process each slice using the image processor associated with the pretrained Google/vit-base-patch16-224-in21k model. Resize each slice to 224 × 224 pixels and normalize the three channels using a mean of [0.5, 0.5, 0.5] and a standard deviation of [0.5,0.5,0.5].
   4. Store the processed sagittal, coronal, and axial slice sequences as S_x, S_y , and S_z  respectively, and use them as inputs for slice-level feature extraction.
2. Generate slice-level feature representations
   1. Use the Google/vit-base-patch16-224-in21k model implemented with the Hugging Face Transformers library as the slice-level feature extractor⁴⁰. Use the model weights pretrained on ImageNet-21k.
   2. Feed the processed three-channel S_x, S_y , and S_z into the ViT model. Divide each input slice into 16 × 16 pixel patches according to the pretrained model configuration.
   3. Process the patch sequence using the 12 Transformer encoder layers and 12 attention heads of the ViT model. Use the configured hidden dimension of 768 and intermediate dimension of 3,072.
   4. Extract the pooler_output of the ViT model as the slice-level feature representation. For the ⁱ -th sagittal slice S_i^x  , calculate the feature vector as:
          (1)
   5. Apply the same feature-extraction procedure to all valid coronal and axial slices.
   6. Stack the sagittal slice features according to their original anatomical order to construct the sagittal feature sequence: . Apply the same operation to the coronal and axial slice features to construct: F_y ∈ R^M_y×768, F_z ∈ R^M_z×768 .
   7. Feed the 768-dimensional slice-feature sequences  F_x, F_y, and F_z directly into the corresponding enhanced sparse multiple instance learning modules.
3. Aggregate slice features using the Enhanced Sparse Multiple Instance Learning module
   Use the Enhanced Sparse Multiple Instance Learning (MIL) module to aggregate the slice feature sequence X  R^B×M_v×D  of each view into a view-level representation. The overall structure of the Enhanced Sparse MIL module is shown in Figure 3.
4. Compute multiscale slice scores
   Compute slice importance scores using the multiscale slice scoring module. The structure of the multiscale slice scoring module is shown in Figure 4.
   1. Use the local scoring branches to calculate score_local ∈  R^B×M_v×1  for individual slices. The branch applies Layer Normalization followed by linear layers with dimensions from 768 to 1, with GELU activations between the linear layers.
   2. Compute the initial global context vector by averaging all slice features according to equation (2).
      global_context = mean(X,dim = 1) ∈ R^B×D  (2)
   3. Process the global-context vector using the context encoder shown in Figure 4. Apply Layer Normalization, a linear projection from 768 to 128 dimensions, a GELU activation function, and a linear projection from 128 back to 768 dimensions.
   4. Replicate the enhanced global-context vector M_v times along the slice dimension.
   5. Use the replicated global context to calculate a scalar global importance score score_global ∈ R^B×M_v×1  for each slice. The global scorer uses the same network structure as the local scorer.
   6. Combine the local and global importance scores using a learnable view-specific coefficient:
       score_combined,i =γ ⋅ score_local,i + (1-γ) ⋅ score_global,i  (3)
   7. where γ∈[0,1]  is a learnable parameter used to balance the contributions of local and global information.
5. Evaluate feature diversity and determine the adaptive keep ratioEvaluate feature diversity using the feature diversity assessment module shown in Figure 5.
   1. Obtain the corresponding slice-feature sequence X_b,v =∈ [x_b,v,1,…,x_b,v,Mv] ∈ R^M_v×D  for each participant b in batch B and each anatomical view v.
   2. Apply L₂ normalization to each slice-feature vector along the feature dimension.
   3. Calculate the pairwise cosine-similarity matrix for all slices within the corresponding participant and view: , where i,j ∈ {1,... ,M_v}  index two slices within the same participant b and anatomical view v. Exclude the diagonal elements with i=j from the subsequent calculation. Generate one cosine-similarity matrix of size  M_v × M_v for each participant and anatomical view.
   4. Set the diagonal elements of the cosine-similarity matrix to zero to exclude the self-similarity of individual slices. Calculate the mean cosine similarity across all off-diagonal ordered slice pairs.
         (4)
   5. Define the feature-diversity score as: . Interpret a larger  d_b,v as lower mean similarity and greater diversity among the slice features, and a smaller d_b,v  as greater feature redundancy. Therefore, for a batch containing B participants, obtain one diversity-score vector for each view:  d_v = [d_1,v,d_2,v,…,d_B,v] ∈ R^B.
   6. Expand their shape from [B] to [B,1] and directly enter them into the adaptive sparsity controller.
   7. Configure the adaptive sparsity controller as a two-layer multilayer perceptron consisting of a linear layer from 1 to 128 dimensions, a ReLU activation, a linear layer from 128 to 1 dimension, and apply the Sigmoid function to obtain the scalar weight  .
   8. Calculate the adaptive keep ratio as:
      adaptive_keep_ratio=clamp(base_keep_ratio + (1-base_keep_ratio)q_b,v,0.45,0.9)  (5)
      where   denotes the baseline slice retention ratio.
      The scaling factor is not independently tuned; it is defined as α = 1 − base_keep_ratio. With base_keep_ratio = 0.45, α = 0.55.
6. Aggregate sparse and dense attention features
   Aggregate slice features using parallel sparse and dense attention pathways. The sparse pathway selects the most informative slices for efficient aggregation, while the dense pathway preserves information from all slices. Fuse the two pathways through a residual connection to improve robustness.
   1. For each sample in the batch, dynamically determine the number of retained slices K=int(M_v⋅adaptive_keep_ratio), according to its  adaptive_keep_ratio. 
   2. Compute sparse attention features, assign attention weights to the selected slices according to equation (6).
          (6)
      where τ₁  denotes the temperature parameter.
   3. Obtain the sparse view-level feature by weighted summation of the selected slice features, as shown in equation (7):
         (7)
   4. Compute dense attention features, assign attention weights to all M_v slices according to equation (8).
         (8)
   5. Obtain the dense view-level feature by weighted summation of all slice features, as shown in equation (9):
         (9)
   6. Fuse the sparse and dense aggregation features through a residual connection using a learnable parameter β ∈ [0,1] . Compute the final view-level aggregated feature according to Equation (10):
      f_v,aggregated =f_v,sparse+ β⋅f_v,dense ∈ R^D  (10)
   7. Apply shared layer normalization and linear projection to map each aggregated view-level feature from 768 to 512 dimensions for subsequent multiview and whole-brain feature fusion.
7. Extract whole-brain volumetric features using a 3D-CNN network
   1. Input each preprocessed whole-brain MRI volume into the CNN_Bone backbone to extract three-dimensional volumetric features. Use the network architecture summarized in Table 2.
   2. Construct CNN_Bone using four sequential ConvLayer modules. In each module, apply three-dimensional convolution, three-dimensional max pooling, batch normalization, and ReLU activation according to the parameters listed in Table 2. Progressively increase the feature-channel number from num to 8 × num, while reducing the spatial dimensions through max pooling.
   3. Retain the batch dimension and flatten all channel and spatial dimensions of the feature map output by the fourth ConvLayer into a one-dimensional feature vector. 
   4. Apply Layer Normalization and linear projection to map the flattened feature vector to 512 dimensions for subsequent multiview and whole-brain feature fusion.
8. Fuse multidimensional features using an adaptive gating module
   Use an adaptive gating module to fuse multiview slice features and whole-brain volumetric features. The fusion process consists of three steps: feature quality assessment, adaptive gating weight generation, and weighted feature fusion. By dynamically assigning fusion weights to different feature dimensions, the module enables the model to emphasize more informative representations for each subject.
   1. Assess feature quality
      1. Obtain four 512-dimensional feature vectors from the previous stage  f_x, f_y, f_z, f_3D ∈ R⁵¹². Apply L2 normalization to all feature vectors before feature fusion, using 10^-6  for numerical stability.
      2. Assign structurally identical but independently parameterized feature-informativeness assessors Q_v to the four branches. Configure each assessor as a linear layer from 512 to 128 dimensions, a GELU activation, a linear layer from 128 to 1 dimension, and a sigmoid output.
         s_v = Q_v (f_v) ∈ (0,1)  (11)
         Define s_v as the feature-informativeness score of the branch for diagnostic classification. 
         NOTE: The feature-informativeness score is learned through the classification cross-entropy loss without independent image-quality labels.
      3. Concatenate the four branch scores into a four-dimensional score vector:
         S = [s_x,s_y,s_z,s_3D] ∈ R⁴              
   2. Generate adaptive gating weights
      1. Enter the score vector S into the gating network G_net . Configure G_net  as a linear layer from 4 to 16 dimensions, a ReLU activation, and a linear layer from 16 to 4 dimensions. The gating network    generate raw gating logits, as shown in equation (12):
         Logits = G_net(S) ∈ R⁴  (12)
      2. Apply temperature-scaled Softmax across the four branches to obtain nonnegative gating weights that sum to 1.
            (13)
   3. Perform adaptive feature fusion
      1. Calculate the weighted sum of the four normalized branch features.
            (14)
         Use F_fused as the final fused feature representation.
9. Train and evaluate MDFAG-Net
   1. Train MDFAG-Net
      1. Partition the 842 subjects at the subject level using stratified sampling according to the NC, MCI, and AD labels and a random seed of 42. Assign subjects to the original training, validation, and independent test sets, respectively. Report the diagnostic-class distributions and proportions in Table 3.
      2. Keep the axial, coronal, and sagittal slices and the 3D MRI volume from each subject in the same data subset to prevent subject-level data leakage.
      3. Merge the original training and validation sets to form a development set of 674 subjects. Divide the development set into five folds at the subject level, using four folds for training and one fold for internal validation in each iteration.
      4. Implement MDFAG-Net using PyTorch on a workstation equipped with an NVIDIA GPU with 32 GB of memory. Set the random seed to 42.
      5. Train MDFAG-Net for 50 epochs using the Adam optimizer, an initial learning rate of 1 × 10^-4 , a batch size of 16, and the cross-entropy loss function.
      6. Initialize τ₁, γ , β, and base_keep_ratio in the Enhanced Sparse MIL module to 1.2, 0.5, 0.3, and 0.45, respectively. Restrict the adaptive keep ratio to 0.45–0.9, and τ₂ initialize   in the adaptive gating module to 1.0.
      7. Determine the principal hyperparameters using only the internal-validation data, and retain the setting achieving the highest validation accuracy. Keep the independent test set excluded from hyperparameter selection and model training.
      8. Save the model checkpoint achieving the highest internal-validation accuracy in each fold.
   2. Evaluate classification performance
      1. Load the checkpoint with the highest internal-validation accuracy from each fold and evaluate it on the fixed independent test set.
      2. Calculate accuracy, macro-averaged precision, macro-averaged recall, macro-averaged F1 score, and macro-averaged AUC(OvR). Calculate accuracy, precision, recall, and F1 score using Equations (15)–(18). 
            (15)
            (16)
            (17)
             (18)
      3. Construct each comparison model according to its original publication, official public implementation, or standard-framework implementation, and retrain or fine-tune it using the training data in the present study. Retain the original network architecture and modify only the input interface and classification head required for the NC/MCI/AD three-class classification task. Report the implementation source, input configuration, initialization, task-specific adaptation, and selected learning rate of each method in Table 4^{3,5,8–10,13,15,16,20–22}.
      4. Evaluate all comparison methods using the same subject-level data partitions, MRI preprocessing pipeline, five-fold cross-validation procedure, independent test set, and evaluation metrics. Fix the optimizer category, batch size, loss function, maximum number of training epochs, and random seed across all methods.
      5. Apply the same prespecified candidate set {1×10^-5,5×10^-5,1×10^-4,5×10^-4}  of initial learning rates to all methods. Select the final learning rate and model checkpoint exclusively according to the highest internal-validation accuracy in each fold. Do not use the independent test set for hyperparameter tuning or model selection.
      6. Define the macro-averaged AUC as the primary metric for statistical comparison. Compare each baseline method with MDFAG-Net using a two-sided paired t-test based on the fold-matched macro-AUC results. Report the exact p-values and define statistical significance as p < 0.05.

4.Perform hyperparameter sensitivity analysis

1. Perform one-factor-at-a-time sensitivity analyses for base_keep_ratio, τ₁, and τ₂ on the development set using the same five-fold cross-validation protocol as the main experiments. Vary one parameter at a time while keeping the network architecture, data splits, and remaining training settings unchanged.
2. Set base_keep_ratio sequentially to {0.30,0.35,0.40,0.45,0.50,0.55} . Evaluate τ₁  and τ₂ separately at  {0.6,0.8,1.0,1.2,1.4,1.6}. Complete five-fold training and internal validation for each parameter setting, and record the Accuracy and Macro-AUC.
3. For each τ₁setting, extract the normalized attention weights of the retained slices generated by Equation (6) in the Enhanced Sparse MIL module. For a single view containing Kretained slices, calculate the normalized attention entropy as:
      (19)
   where  a_i denotes the normalized attention weight of the i-th retained slice, K denotes the actual number of retained slices. 
4. Calculate the entropy-based attention sparsity index as: S_att=1-H_norm  , where a higher S_att  indicates a more concentrated and sparse attention distribution, whereas a lower value indicates a more uniform attention distribution.

5.Perform model interpretability analysis

1. Perform slice-level multiview Grad-CAM analysis
   1. Process all valid axial, coronal, and sagittal MRI slices in their original anatomical order using the corresponding ViT and Enhanced Sparse MIL modules. For each subject and view, determine the retained slices according to the slice-importance ranking and adaptive_keep_ratio.
   2. Summarize the adaptive keep ratio and the actual number of retained slices separately for the axial, coronal, and sagittal views, and further stratify the results by the NC, MCI, and AD diagnostic groups. 
   3. Randomly select one subject from each diagnostic group using a fixed random seed. For each selected subject, select the retained slice with the highest slice-importance score from each anatomical view.
   4. Generate the Grad-CAM41 maps using the final Transformer encoder layer. Remove the CLS token, reshape the remaining 196 patch tokens into a 14 × 14 two-dimensional spatial layout, and resize the maps to 224 × 224 using nearest-neighbor interpolation. Normalize each map individually and overlay it on the corresponding MRI slice using semi-transparent visualization.
   5. Use the Hammersmith 95-region atlas to identify the anatomical regions corresponding to each selected slice. Calculate the mean normalized Grad-CAM response within each region and identify the region with the highest response. Compare the identified regions with previously reported Alzheimer’s disease-associated structures to assess the biological plausibility of the selected high-importance slices.
2. Perform anatomy-based 3D whole-brain DeepSHAP analysis
   1. Load the trained MDFAG-Net model and process all subjects in the test set in evaluation mode. Using a fixed random seed, randomly select five MRI volumes from the NC subjects in the training set as a common reference background, and use the same background set for all DeepSHAP computations.
   2. For each subject, precompute and fix the axial, coronal, and sagittal features, and use only the 3D MRI volume as the input to be explained.
   3. Use block2conv of the 3D CNN branch as the intermediate explanation layer6,42. Calculate class-specific SHAP values for the NC, MCI, and AD outputs and convert them into three-dimensional attribution volumes.
   4. Map the attribution volumes to the preprocessed MRI space and parcellate the whole brain using the Hammersmith 95-region atlas. Sum the absolute voxel-level SHAP values within each region to obtain region-level attribution scores.
   5. Group the test subjects according to their predicted classes. Average the corresponding class-specific regional scores within the predicted MCI and AD cohorts, rank the 95 regions in descending order, and display the 15 highest-contributing regions for each cohort.

Results

Classification performance comparison

Perform anatomy-based 3D whole-brain DeepSHAP analysis

The classification performance of MDFAG-Net was compared with representative MRI-based methods for Alzheimer’s disease classification, including 3D-CNN, ResNet34, DenseNet121, PlgFormer, PDMML, 3D Swin Transformer, 2D-CNN, ViT, BrainMAE, MSA3D, and PMF-Net. All methods were evaluated on the same ADNI cohort using identical subject-level data partitions...

Discussion

The proposed MDFAG-Net achieved improved classification performance compared with representative 2D, 3D, and multibranch methods on the ADNI dataset. Ablation experiments demonstrated the contributions of Enhanced Sparse MIL and Adaptive Gating, supporting the effectiveness of adaptive slice aggregation and dynamic 2D–3D feature fusion. Grad-CAM and DeepSHAP further highlighted anatomically meaningful structures, including the hippocampus, amygdala, posterior cingulate gyrus, parahippocampal gyrus, and ventricular ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset	Alzheimer's Disease Neuroimaging Initiative	https://adni.loni.usc.edu/	Source of T1-weighted structural MRI data for NC, MCI, and AD classification
FMRIB Software Library (FSL)	University of Oxford	Version 6.0 / https://fsl.fmrib.ox.ac.uk/fsl/docs/	MRI preprocessing, including skull stripping, spatial registration, bias field correction, and brain segmentation
Hammersmith adult brain atlas (95 ROIs)	Imperial College London	https://brain-development.org/brain-atlases/adult-brain-atlases/	Anatomical parcellation for ROI-based Grad-CAM and DeepSHAP analyses
Python	Python Software Foundation	Version 3.9	Programming environment
PyTorch	PyTorch Foundation	PyTorch 2.1.0	Deep learning framework used to implement and train MDFAG-Net
ViT-Base-Patch16-224-in21k	Google Research	https://github.com/google-research/vision_transformer	Pretrained Vision Transformer used for slice-level feature extraction
NVIDIA vGPU	NVIDIA	NVIDIA Tesla V100-SXM2-32G	GPU platform used for model training and inference